Anomaly Detection is one of the most challenging tasks in the field of machine-learning which naturally makes it particularly interesting for researchers[1]}, [2]}.
Various variants of anomaly detection algorithms are being used in the field of signal processing[3]}, [4]}, medical diagnosis[5]}, network intrusion detection [6]}, [7]}, [8]}, and video surveillance[9]}, [10]}.
Anomaly detection systems, although difficult to train, are expected to have high efficiency as such models are expected to reduce human labor for their corresponding domains.
Specifically, in video surveillance for the purpose of safety and security, autonomous anomaly detection systems contribute significantly by analysing hundreds of concurrent video streams which otherwise would take a lot of human resources as well as attention.
With the recent development in deep-learning algorithms it is becoming handy to to apply these low computational yet highly efficient algorithms for the learning problems.2D and 3d convolutions, auto-encoders, object detectors, aaaaa are few among many other algorithms which are being used for anomaly detection.
